Data Management and Bioinformatics Core Project Summary: This innovative integrated systems biology application seeks to delineate the complex host/pathogen interactions occurring at the alveolar level that lead to unsuccessful response to therapy in serious pneumonia. In spite of active research in this area, a generalize multi-scale solution that can support a systems biology approach to clinical problems remains elusive. This is not surprising, given that a general solution effectively requires modeling all of biology and medicine simultaneously. Even though a general solution may be intractable, we believe that substantial progress can be made by focusing on the clinical, genomic, and pathogen data for a single condition, Hospital Acquired Pneumonia (HAP). The overall goal of the Data Management and Bioinformatics (DMBI) Core is to develop and implement new and enhanced computational resources that support a systems biology approach to HAP, and to share those resources broadly. The DMBI Core will sit at the nexus of SCRIPT where it will provide the tools, methods, skills and infrastructure to collect, integrate, transform, analyze and distribute the diverse data generated by both projects. The design and implementation of the DMBI Core is based on the premise that genome-centric approaches and phenome-centric approaches are both inherently scientifically limiting. Rather, a systems biology approach that gives equal weight to all data types is more likely to produce significant findings. Achieving the broader goals of this project will require the seamless integration of clinical data with molecular profiling data on both host and pathogen. We will leverage our extensive experience in the integration of EHR and genomic data to design a novel graph- based repository. The multiple sources of genomic data will be integrated with clinical phenomic data extracted from the continuous stream of data that populates the EHR in patients in the intensive care unit. We will utilize the data sets generated in Aim 1 for further analysis of the gene regulatory networks using both novel computation tools and novel uses of existing tools. In particular, we will develop tailor- made algorithms for an integrative analysis of multiple omics data sets that enable modeling of the underlying gene regulatory networks. A multi-pronged approach to dissemination will be employed. Datasets and metadata will be archived in the appropriate NIAID and/or NCBI archives. Computational tools will be distributed through a GitHub repository. Of greater long term value will be the creation of a secure, interactive web portal that will allow research to interact with the study data repository to not only browse and filter data based on a rich section of attributes, but also to run analysis directly.